Permuted Block-Sparse Attention Reduces LLM Computation
Researchers propose Permuted Block-Sparse Attention, a method to optimize self-attention in large language models by rearranging token order. The approach addresses the O(N²) complexity bottleneck by improving block-level sparsity, reducing memory and latency for long sequences.
Key facts
- Self-attention has O(N²) complexity with respect to sequence length.
- Block-sparse attention partitions sequences into blocks and skips computation for some blocks.
- Important key tokens for queries within a single block may be scattered across many blocks.
- Permuted Block-Sparse Attention rearranges token order to improve sparsity.
- The method aims to reduce computational redundancy in attention mechanisms.
- The technique is designed for large language models with long context lengths.
- The work is published on arXiv with ID 2510.21270.
- The announcement type is replace-cross.
Entities
Institutions
- arXiv